Purpose: To understand how sentiments and discursive patterns expressed in user-generated content (UGC) configure the digital image of the Piauí coast as an emerging tourism destination.
Design/methodology/approach: A convergent mixed-methods design with an exploratory and descriptive scope was adopted. The study analyzed 2,400 TripAdvisor reviews of 16 attractions published between 2011 and 2023. The quantitative dimension combined Descending Hierarchical Classification (DHC), Similarity Analysis and Correspondence Factor Analysis (CFA) in IRAMUTEQ with independent polarity classification using multilingual DistilBERT; the qualitative dimension involved thematic interpretation of the lexical classes. The automated classification was manually validated by two researchers.
Findings: Four thematic categories were identified: nature and ecotourism; leisure and hospitality; culture and historical heritage; and infrastructure and services. The CFA explained 62.82% of total inertia. Automated classification recorded 42.8% positive, 33.8% neutral, and 14.4% negative sentiments; for 9.0% of the reviews, the original classification could not be recovered from the preserved records. Overall, the findings indicate a broadly favorable yet heterogeneous image.
Research limitations/implications: The corpus is restricted to one platform and aggregates reviews over a twelve-year period, introducing temporal heterogeneity; the preserved records do not allow recovery of the validation subsample size or a formal inter-rater agreement coefficient.
Practical implications: The findings support public managers, tourism organizations and cultural agents in prioritizing infrastructure and leveraging the destination’s natural and cultural attributes.
Originality/value: The study combines lexicometric techniques and automated sentiment classification in an underrepresented Brazilian emerging destination, offering an analytical protocol transferable to other contexts.
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